Stacked convolutional and recurrent neural networks for music emotion recognition

This paper studies the emotion recognition from musical
tracks in the 2-dimensional valence-arousal (V-A) emotional
space. We propose a method based on convolutional
(CNN) and recurrent neural networks (RNN), having significantly
fewer parameters compared with state-of-the-art
(SOTA) method for the same task. We utilize one CNN
layer followed by two branches of RNNs trained separately
for arousal and valence. The method was evaluated using
the “MediaEval2015 emotion in music” dataset. We
achieved an RMSE of 0.202 for arousal and 0.268 for valence,
which is the best result reported on this dataset